大数据环境下地铁自动售检票系统设计与实现

近年来,随着经济领域蓬勃发展,我国加快了现代化建设进程,交通设施建设不断推进;受互联网大数据技术变革的影响,传统地铁售检票系统无法满足高客流量、大数据流处理的高强度工作要求;在日常实践应用中,传统地铁售检票系统经常出现检票识别率低、售票信息运算处理响应速度慢、多人员、多任务操作执行准确率差的问题;针对上述问题,结合大数据资源运算能力,提出大数据环境下地铁自动售检票系统设计;采用大数据实名高检处理引擎(VBDKG)、多路分处运算模组(ICGRU)与动态身份比对算法(DBTDE),针对传统地铁自动售检票系统存在的问题进行解决;通过仿真实验测试证明,提出的大数据环境下地铁自动售检票系统设计具有较强的实施性与可操作性;同时,运行处理准确性高,运行稳定。     

自动售检票系统质量保证方法及应用

自动售检票系统简称AFC,是轨道交通机电系统中重要的机电子系统之一,系统终端设备的可靠性直接影响了运营效果和乘客体验度。自动售检票系统的质量保证是产品研发上线的重要保障。基于此,介绍了自动售检票系统质量保证方法及在系统建设上的应用,重点阐述了设计阶段、制造过程、测试验证阶段等三个主要阶段的质量保证方法。     

行业纵深

北京TCC项目签约同方本报讯4月3日获悉,北京市基础设施投资有限公司分别与清华同方股份有限公司、新加坡科技电子有限公司、中铁电气化局集团有限公司、ERG有限责任公司、北京北控电信通信息技术有限公司联合签订了《北京市轨道交通路网管理服务中心工程指挥中心系统采购合同》及《北京市轨道交通路网管理服务中心工程自动售检票清算管理中心系统建设合同》。北京市轨道交通指挥中心(TCC)、自动售检票清算管理中心(ACC)系统集成项目将于2008年8月奥运会前投入运营,作为全国首个地铁网络化运营的指挥中心和清算管理中心,将承担北京市轨…     

城市轨交自动售检票车站管理系统的研究与实践

城市轨道交通自动售检票车站管理系统在轨道交通运营中直接面向乘客,并在线路自动售检票系统中承担着承上启下的关键作用。从车站管理系统的功能需求出发,结合自动售检票系统具有分层系统的结构特征,对车站管理系统的构架模式和参考模型进行研究和设计,提出关于XML规范接口协议简约与重构的技术思路,描述异构平台数据收发的处理流程,并给出基于WPF技术编程实现设备运营监控界面示例。     

轨道交通综合票务管理平台系统设计方案

近年来，随着城市轨道智能支付科技的逐步推进，逐渐摆脱原来自动售检票系统等级架构设计的运营模式，为进一步提升总体运营高效率，趋向扁平化设计管理的模式慢慢被大众接纳。实现新型网络支付在城市轨道自动售检票行业统一运用，该文对构建城市轨道交通综合性票务管理服务平台的重要性及其管理平台整体架构、基本功能等展开分析。     

GE Proficy HMI/SCADA iFIX软件在南京地铁一、二号线自动售检票(AFC)系统中应用

GE智能平台的Proficy HMI/SCADA-iFIX软件获选应用于近日全线开通运营的南京地铁一号线的收费系统(AFC系统)。作为国内最早的IC卡自动售检票系统,南京地铁一号线的售检票系统已经运行超过三年。经过几年的使用实践,以具有实时通信特点的Proficy HMI/SCADA–iFIX作为内核的AFC系统稳定、安全、可靠,得到了业主单位的广泛好评。     

地铁AFC系统中的关键技术分析

随着城市发展水平的不断提高,交通拥挤问题逐渐凸显出来,为了有效提高城市交通系统的运载能力和综合服务质量,修建地铁已经成为许多城市的优先选择。自动售检票系统(AFC)能够将地铁中售票、检票以及票务管理通过网络技术等手段实现自动化控制,大大提高运营单位的运输效率,也方便乘客的出行。本文在对地铁AFC系统进行概述的基础上,对地铁AFC系统的总体结构和关键技术进行了简要探讨。     

轨道交通综合监控多线AFC接入协议

目前我国轨道交通事业迅猛发展,各城市的地铁线路都分别建成了轨道交通综合监控系统,但是,一条线路的地铁综合监控系统只能监控本条地铁线路系统内的信息,为了突破此一监控现状的局限性,将各条地铁线路的信息综合至同一个后台进行信息收集、监视控制已成为当前轨道交通行业内的最新最先进的运营发展模式,本文描述了一种轨道交通综合监控系统(IMS)与自动售检票(AFC)专业特殊接口协议方案,通过IMS与ACC的信息交互,使得可以在一个统一的监控平台监视南京地铁1号线、南京地铁1号线南延线以及南京地铁2号线全部的自动售检票系统综合信息。     

大连地铁2号线自动售检票线路中央计算机系统概要设计

介绍了大连地铁2号线自动售检票线路中央计算机系统（LCC）的概要设计,对大连地铁2号线自动售检票线路中央计算机系统（LCC）的总体框架进行描述,包括LCC系统的业务需求及功能模块划分、系统的软件及物理架构设计、系统的设计原则及接口协议等,概要设计作为需求分析、系统详细设计和开发的桥梁,为后续系统的详细设计提供设计基础和依据。     

基于Linux系统的旋转门检票机设计与研究

设计的旋转门检票机是基于Linux系统的一个实例。从旋转门检票机硬件设计方面,详细介绍了旋转门检票机构成、关键模块旋转门模块的设计和通道逻辑处理模块的实现。从旋转门检票机的软件设计方面,详细介绍了旋转门检票机软件架构和基于Linux操作系统的检票机应用程序开发。为城市轨道交通自动售检票系统终端设备的自主研发提供经验。     

基于云理论的网格资源选择

资源选择是影响网格调度和系统效率的关键,针对网格资源选择中用户对服务质量(QoS)的定性描述和调度的自私性,提出了利用云理论实现资源选择的方法。在深入分析QoS参数的云理论模型基础上,提出了以资源代理实现云模型资源选择的体系结构,设计了相应的调度算法。实验表明,该算法在资源调度率和吞吐量以及系统资源的利用效率等方面体现出良好的特性,同时克服了用户定义QoS参数的困难,达到了优化调度的目的。     

Privacy-preserving data utilization in hybrid clouds

As cloud computing becomes prevalent, more and more sensitive data is being centralized into the cloud, which raises a new challenge on how to utilize the outsourced data in a privacy-preserving manner. Although searchable encryption allows for privacy-preserving keyword search over encrypted data, it could not work effectively for restricting unauthorized access to the outsourced private data. In this paper, aiming at tackling the challenge of privacy-preserving utilization of data in cloud computing, we propose a practical hybrid architecture in which a private cloud is introduced as an access interface between the data owner/user and the public cloud. Under this architecture, a data utilization system is provided to achieve both exact keyword search and fine-grained access control over encrypted data. Security and efficiency analysis for the proposed system are presented in detail. Then, further enhancements for this system are considered in two steps. (1) We show how to extend our system to support efficient fuzzy keyword search while overcoming the disadvantage of insignificant decryption in the existing privacy-preserving fuzzy keyword search scheme. (2) We demonstrate approaches to realize an outsourcing cryptographic access control mechanism and further reduce the computational cost at the data user side.     

基于云网络架构的虚拟网络映射算法研究

云网络架构采用控制与转发分离机制实现了资源的灵活分配,为了满足云网络架构的资源分配符合多业务的资源需求,提出了一种基于云网络架构的虚拟网络映射算法,提高了资源利用率。建立的虚拟网络映射算法模型,给出了虚拟网络映射算法的约束条件和优化目标。针对语音、视频和数据3种业务进行了仿真,结果表明,提出的算法提高了控制资源利用率、转发资源利用率和链路资源利用率。     

Preemptive cloud resource allocation modeling of processing jobs

Cloud computing allows execution and deployment of different types of applications such as interactive databases or web-based services which require distinctive types of resources. These applications lease cloud resources for a considerably long period and usually occupy various resources to maintain a high quality of service (QoS) factor. On the other hand, general big data batch processing workloads are less QoS-sensitive and require massively parallel cloud resources for short period. Despite the elasticity feature of cloud computing, fine-scale characteristics of cloud-based applications may cause temporal low resource utilization in the cloud computing systems, while process-intensive highly utilized workload suffers from performance issues. Therefore, ability of utilization efficient scheduling of heterogeneous workload is one challenging issue for cloud owners. In this paper, addressing the heterogeneity issue impact on low utilization of cloud computing system, conjunct resource allocation scheme of cloud applications and processing jobs is presented to enhance the cloud utilization. The main idea behind this paper is to apply processing jobs and cloud applications jointly in a preemptive way. However, utilization efficient resource allocation requires exact modeling of workloads. So, first, a novel methodology to model the processing jobs and other cloud applications is proposed. Such jobs are modeled as a collection of parallel and sequential tasks in a Markovian process. This enables us to analyze and calculate the efficient resources required to serve the tasks. The next step makes use of the proposed model to develop a preemptive scheduling algorithm for the processing jobs in order to improve resource utilization and its associated costs in the cloud computing system. Accordingly, a preemption-based resource allocation architecture is proposed to effectively and efficiently utilize the idle reserved resources for the processing jobs in the cloud paradigms. Then, performance metrics such as service time for the processing jobs are investigated. The accuracy of the proposed analytical model and scheduling analysis is verified through simulations and experimental results. The simulation and experimental results also shed light on the achievable QoS level for the preemptively allocated processing jobs.     

High-speed processing in wired-and-wireless integrated autonomous decentralized system and its application to IC card ticket system

The automatic fare collection systems need both high performance and high reliability. High performance is one of the most expected functions on the automatic fare collection gates (AFCGs) handling the highly dense passengers during rush hours. Reliability is also indispensable because the tickets are equivalent to money. For a wireless IC card ticket system, expected to improve the passengers’ convenience and to reduce the maintenance cost, is difficult to meet these two requirements because of wireless communications between an IC card and an AFCG. This paper introduces the autonomous decentralized system as the solution and how it is applied to the system. Then, two models are prepared and simulated to evaluate the efficiency, especially high-speed processing. The technologies are implemented into the “Suica” system at East Japan Railway Company and have proven the effectiveness.     

基于云计算的政府信息资源协同共享及实现途径

云计算的应用为政府信息资源协同与共享带来了新思路,文章分析了基于云计算的政府信息资源开发的环境和特征。梳理了其协同开发的体系架构和业务逻辑。在总结基于云计算的信息资源开发利用的最新进展基础上,就政府部门如何推动信息资源社会化开发提出了政策建议。     

Performance analysis based resource allocation for green cloud computing

Cloud computing has become a new computing paradigm that has huge potentials in enterprise and business. Green cloud computing is also becoming increasingly important in a world with limited energy resources and an ever-rising demand for more computational power. To maximize utilization and minimize total cost of the cloud computing infrastructure and running applications, resources need to be managed properly and virtual machines shall allocate proper host nodes to perform the computation. In this paper, we propose performance analysis based resource allocation scheme for the efficient allocation of virtual machines on the cloud infrastructure. We experimented the proposed resource allocation algorithm using CloudSim and its performance is compared with two other existing models.     

CBC: Caching for cloud-based VOD systems

Cloud-based video on demand (VOD) service is a promising next-generation media streaming service paradigm. Being a resource-intensive application, how to maximize resource utilization is a key issue of designing such an application. Due to the special cloud-based VOD system architecture consisting of cloud storage cluster and media server cluster, existing techniques such as traditional caching strategies are inappropriate to be adopted by a cloud-based VOD system directly in practice. Therefore, in this study, we have proposed a systemic caching scheme, which seamlessly integrates a caching algorithm and a cache deployment algorithm together to maximize the resources utilization of cloud-based VOD system. Firstly, we have proposed a cloud-based caching algorithm. The algorithm models the cloud-based VOD system as a multi-constraint optimization problem, so as to balance the resource utilization between cloud storage cluster and media server cluster. Secondly, we have proposed a cache deployment algorithm. The algorithm further manages the bandwidth and cache space resource utilization inside the media server cluster in a more fine-grained manner, and achieves load balancing performance. Our evaluation results show that the proposed scheme enhances the resource utilization of the cloud-based VOD system under resource-constrained situation, and cuts down the reject ratio of user requests.     

Combined Cloud: A Mixture of Voluntary Cloud and Reserved Instance Marketplace

Voluntary cloud is a new paradigm of cloud computing. It provides an alternative selection along with some well-provisioned clouds. However, for the uncertain time span that participants share their computing resources in voluntary cloud, there are some challenging issues, i.e., fluctuation, under-capacity and low-benefit. In this paper, an architecture is first proposed based on Bittorrent protocol. In this architecture, resources could be reserved or requested from Reserved Instance Marketplace and could be accessed with a lower price in a short circle. Actually, these resources could replenish the inadequate resource pool and relieve the fluctuation and under-capacity issue in voluntary cloud. Then, the fault rate of each node is used to evaluate the uncertainty of its sharing time. By leveraging a linear prediction model, it is enabled by a distribution function which is used for evaluating the computing capacity of the system. Moreover, the cost optimization problem is investigated and a computational method is presented to solve the low-benefit issue in voluntary cloud. At last, the system performance is validated by two sets of simulations. And the experimental results show the effectiveness of our computational method for resource reservation optimization.